Explain optimization and tensor vs pipeline parallelism
Overview
This question evaluates knowledge of deep learning optimization techniques (quantization, pruning, knowledge distillation, kernel/operator fusion, memory and throughput optimizations) and model parallelism strategies (tensor versus pipeline), measuring competency in performance engineering, scalability, communication patterns and system-level trade-offs for training and inference. It is commonly asked to assess an engineer's ability to reason about practical and conceptual trade-offs, collectives, bottlenecks and mitigations in distributed and resource-constrained environments, and it falls under the Machine Learning/Deep Learning domain requiring both conceptual understanding and practical application.
Task: Deep Learning Optimization and Parallelism
You are asked to explain optimization techniques commonly used to improve deep learning training and inference. Address the following:
Part A: Optimization Techniques
Describe common AI optimization techniques for both training and inference. For each technique, state:
- Goal(s)
- How it works (at a high level)
- Typical benefits
- Trade-offs and pitfalls
Cover at least these categories and examples:
- Quantization (e.g., INT8, FP8, PTQ vs QAT)
- Pruning (unstructured vs structured, N:M sparsity)
- Knowledge distillation (teacher–student)
- Kernel/operator fusion (e.g., bias+GELU, FlashAttention)
- Memory optimizations (e.g., activation checkpointing, sharding/offload, KV cache)
- Throughput/latency optimizations (e.g., mixed precision, CUDA Graphs/compilation, batching, overlap of compute/comm)
Part B: Model Parallelism Comparison
Compare tensor parallelism and pipeline parallelism:
- How each works
- Communication patterns and collectives used
- When to use each (practical scenarios)
- Typical performance bottlenecks and how to mitigate them
Solution
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